sklearn.datasets.load_iris()

参数：

• return_X_y ；  bool, default=False   ：If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target object.
• as_frame ；  bool, default=False  ：If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric). The target is a pandas DataFrame or Series depending on the number of target columns. If return_X_y is True, then (data, target) will be pandas DataFrames or Series as described below.

返回：

• data{ndarray, dataframe} of shape (150, 4)

The data matrix. If as_frame=True, data will be a pandas DataFrame.

• target: {ndarray, Series} of shape (150,)

The classification target. If as_frame=True, target will be a pandas Series.

• feature_names: list

The names of the dataset columns.

• target_names: list

The names of target classes.

• frame: DataFrame of shape (150, 5)

Only present when as_frame=True. DataFrame with data and target.

New in version 0.23.

• DESCR: str

The full description of the dataset.

• filename: str

The path to the location of the data.

• (data, target)  tuple if return_X_y is True

例子：

from sklearn import  datasets

iris_data = datasets.load_iris()
print(iris_data.data) #返回样本数据
print(iris_data.data.shape) #返回样本数据形状
print(iris_data.target) #返回样本数据的label
print(iris_data.target.shape) #返回样本数据lable形状
print(iris_data.target[[0,50,149]]) #返回样本数据 x_0,x_50 ,x_149 的label
print(iris_data.target_names) #返回lable 名称
print(iris_data.target_names[0:4])
print(iris_data.target_names[[0,1,2]])
print(iris_data.feature_names) #返回特征名称
print(iris_data.feature_names[0:2]) #返回特征名称
print(iris_data.DESCR) #数据描述
print(iris_data.filename) #返回数据存放位置
X ,y = datasets.load_iris(return_X_y=True) 
print(X.shape)
print(y.shape)
iris_data = datasets.load_iris(as_frame=True)
print(type(iris_data))